Evaluating the captioning model

The evaluation function is similar to the training loop, except we don't use teacher forcing here. The input to the decoder at each time step is its previous predictions, along with the hidden state and the encoder output.

A few key points to remember while making predictions:

• Stop predicting when the model predicts the end token
• Store the attention weights for every time step

Let’s define the evaluate() function:

def evaluate(image):
 attention_plot = np.zeros((max_length, attention_features_shape))

 hidden = decoder.reset_state(batch_size=1)

 temp_input = tf.expand_dims(load_image(image)[0], 0)
 img_tensor_val = image_features_extract_model(temp_input)
 img_tensor_val = tf.reshape(img_tensor_val, (img_tensor_val.shape[0], -1, img_tensor_val.shape[3]))

 features = encoder(img_tensor_val)

 dec_input = tf.expand_dims([tokenizer.word_index...